Google’s search engine ranking algorithm is continually evolving as new advances in machine learning, and artificial intelligence is made. In the recent past, Google would rank content based on keywords, and articles mentioning the keywords used in a search would then come up in search engine result pages (SERPs).
The problem, however, is that people think differently than computers. For instance, many people tend to submit queries in the form of questions (natural language processing). Other people want to include extensive information in their search, and some searches also make no sense in a sentence. For these reasons, Google developed semantic technologies to recognize the topics people ultimately search for when they enter a query. As a result, semantic SEO is a concept that anyone concerned about their Google search rankings should understand thoroughly.
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What Is Semantic SEO?
In general, semantic SEO essentially involves attempting to rank content for specific topics instead of using keywords. Semantic SEO strategy seeks to rank content based on how Google can recognize synonyms, different word cases, and the more profound meaning for a query. In semantic SEO, different variations of a keyword are usually ignored. For instance, the terms “search engine” and “search engines” ultimately have the same meaning, so Google would be likely to return the same results for both searches. As a result, you would want to focus on creating an article about search engines rather than concentrating on using the term “search engines” in a specific grammatical case.
With semantic SEO, keywords still matter to some extent, but they are not nearly as crucial as recently. Google still needs keywords to know what your content is about, but you no longer need to create and optimize separate articles for each keyword variation. In some instances, you can even rank for keywords you have not mentioned in your content by using semantic SEO.
It is also essential to keep in mind that semantic SEO is not optional. When Google first released its semantic search algorithm in 2013, over 90 percent of search results were impacted. Today, the algorithm has become much more sophisticated, and nearly all search results are affected. As a result, you can only expect to get good SEO results correctly when you use semantic SEO.
How Does Semantic SEO Differ From Ordinary SEO?
Semantic SEO is different from ordinary SEO because it is focused on topics instead of keywords. Although keywords still matter, the deeper meaning of the topics your content centers around ultimately determines your rankings. One of the things that strengthen the relevance of a page is Latent Semantic Indexing (LSI Keywords), or in other words, supplementary keywords that contribute to the topic. Consequently, simply stuffing keywords into an article can yield negative results. After all, Google can now make sense of keywords well enough to know when to use them intentionally. This includes verbs, adjectives, related questions and phrases, subtopics.
To use an example, If you were writing an article about building a home, it would make sense to mention basements, shingles, drywall, carpeting, kitchens, bedrooms, and other topics that would be essential when building a home. Google’s semantic algorithm knows which words are associated with any given topic, and it expects to see those words mentioned in an article on that topic.
Topical Relevance Is Key in Semantic SEO
Instead of making your article relevant to a specific search query, you should try to mention terms pertinent to the topic you are attempting to rank for. When your article covers the correct issues relevant to your topic, it will have a much higher chance of achieving superior search results. Your content marketing strategy must incorporate relevant words as well as information that is easily understood and make sure that we have good title tags, headers, image alts and internal links as well as anchor text, include all those that play a role as well.
Most importantly, using semantic SEO correctly can increase the certainty of your article ranking for the topic you are targeting. In the past, many SEO professionals would attempt to create an article targeting a specific keyword, but the article would later start ranking for unrelated topics. Today, articles that genuinely focus on a particular topic will almost always rank for that topic. You may get some traffic from irrelevant long-tail search terms, but most of your traffic will come from the main topic you are optimizing for.
Understanding how to create topically relevant articles takes time, but doing so becomes straightforward over time. In many cases, you will look at other articles ranking for your keyword to see what terms Google believes are relevant to that topic. Of course, you will also want to list what specific queries Google considers appropriate to your topic.
It is essential to keep in mind that almost a decade after the release of Google’s semantic SEO algorithm (hummingbird 2013, Rankbrain 2015, Bert 2019), it is still incorrect about topical relevance in many cases. Different queries with identical search results are not always synonymous, so there are sometimes opportunities to create content targeting the subset of visitors looking for answers about something other than what appears in current results. Over time, your content could eventually help to train Google’s algorithm in a way that makes your content the top search result. In other cases, your content could appear on the first page simply because your content appeals to the subset of users searching for the alternative meaning of a search query.
What Are Knowledge Graphs?
Google Knowledge Graph is a database of knowledge that Google has developed. The primary use of Knowledge Graph is to display quick answers to common queries, often in the form of a featured snippet. In the past, online users would have to click through to a website to get straightforward answers, and Google realized that many searchers were wasting minutes scanning articles to find a simple piece of information. Therefore, Google now displays much of its Knowledge Graph on results pages to provide quick answers. Rankings are metrics. For some people, they’re vanity metrics. But vanity metrics are a terrible indicator of how well your website is doing on the search engines.
Google Knowledge Graph is based on a database of knowledge that Google has built over time. For instance, for a company, Google might know the year the company was founded, the name of its current CEO, and the location of its headquarters. Google knows what information people are most interested in when searching for that company by looking at search data. As a result, if you search for “Amazon,” you would see a knowledge panel on the side of the results page that explains basic facts about what Amazon offers, who is behind the company, how long the company has been around, and other critical information about the company.
Knowledge Graph is significant in semantic SEO because it helps to determine the relevance of your content. If you wrote an article about the history of Amazon, Google would be able to identify what your article is about when you mention the specific year the company was founded, the name “Jeff Bezos,” the term “AWS,” and other topics that Google recognizes as being associated with Amazon. Although it is not believed that Google can fact-check articles with this technology, it is vital to use factual information so that Google knows your article is associated with the topic you are targeting.
Understanding How to Use a Pillar Cluster
In semantic SEO, it is often necessary to mention a comprehensive range of topics. If you researched the keywords you are attempting to rank for; you might need to cover thousands of content topics. Of course, there are limits to how long you should make an article. Creating a web page with a million words, for instance, would take a lot of time, introduce performance issues, and inconvenience your visitors.
Instead of including every topic on a single page, you can use the pillar cluster model to transform your website into a content hub by dividing your information across many pages. In pillar clustering, you create one main article for the big keyword you are ultimately trying to rank for. You can then create one paragraph for each topic and link to articles covering specific topics in extensive detail. In this way, you can enable your visitors to understand their query’s main topic while allowing them to learn about a particular issue on a more granular level by going to a separate article.
The best part about a pillar cluster is that it allows Google to associate your pillar article with all topics related to your primary keyword. This organization strategy also helps users since it enables them to learn more about a subject without reading an enormous article.
What Are Entities & Attributes?
In semantic SEO, entities are specific things or facts. Entities are similar to nouns, but they are specifically nouns that can help to identify something.
Attributes are words used to describe the state that an entity is in. Not all attributes are necessarily associated only with the primary entity, but attributes can be terms that are unique to an entity. For instance, “iPhone” attributes might include space gray, FaceID, OLED, or dual-camera system. Most importantly, using a specific pattern of attributes can tell Google whether you discuss iPhones in general or a particular iPhone model.
Entities and attributes have important implications in SEO. There are some niches where you want to mention specific attributes to help Google determine your content’s topical relevance. For instance, if you were writing a review of the latest iPhone, you would want to be sure to mention specific numbers, features, and trade names associated with that product. On the other hand, if you were writing a review of iPhones in general, you would want to talk mainly about topics that have been relevant since iPhones were first released while avoiding an emphasis on specific features in the latest model.
How to Write Semantic SEO Content
When creating semantic SEO content, it is crucial to conduct extensive research from the beginning. In the past, research was much easier because you mainly just needed to find what keywords you should create content about. With semantic SEO, you need first to discover what keywords you want to rank for, and you have to conduct keyword research on that topic to determine what facts and terms focus on your content.
It is essential to understand that keywords are still crucial in semantic SEO. Google has publicly stated that it still relies on mentions of your target keyword search intent to determine relevance. Therefore, you should still mention the specific keywords and topics you are targeting in your content. However, you no longer need to use keyword strings that make no sense, and you should understand the implications of Google’s ability to understand synonyms. For instance, if you identify a list of 10 keyword searches that Google perceives as synonymous, you only need to mention one of them in your article. Furthermore, mentioning all ten keyword searches could be a red flag that you are essentially keyword stuffing.
Using Schema to Improve Your SEO
Finally, you should understand that schema tags and structured data have become more critical than ever before in SEO. Google strongly relies on schema tags to determine the relevance of your content. Although you should never abuse schema tags to improve your rankings, you should extensively use these tags in your HTML to help Google understand your content.
Structured data is also essential because it helps Google understand how to use certain content elements. For instance, using the “HowTo” markup will give Google a greater chance of knowing that your article is meant to provide how-to advice. Although Google may still choose to ignore self-determined markup tags, it does use them extensively when determining relevance. However, always use structured data tags honestly since it is believed that Google has a reputation algorithm that determines how trustworthy your use of structured data is.
Published on: 2021-03-06
Updated on: 2023-03-14